Using Conditional Kernel Density Estimation for Wind Power Density Forecasting

DOI10.1080/01621459.2011.643745zbMath1261.62031OpenAlexW2017468850MaRDI QIDQ4916439

James W. Taylor, Joo Young Jeon

Publication date: 22 April 2013

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: http://opus.bath.ac.uk/36968/1/Jeon_2012_JASA_107_497_66.pdf

zbMATH Keywords

Wind energyKernel estimationPredictive distributionBivariate GARCHWind directionWind speed

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Density estimation (62G07) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to environmental and related topics (62P12) Monte Carlo methods (65C05)

Related Items (12)

Covariate matching methods for testing and quantifying wind turbine upgrades ⋮ Adaptive pointwise estimation of conditional density function ⋮ The uncertainty of conditional returns, volatilities and correlations in DCC models ⋮ Evaluating the impacts of climate change on diurnal wind power cycles using multiple regional climate models ⋮ Probabilistic Forecast Reconciliation under the Gaussian Framework ⋮ Probabilistic wind speed forecasting on a grid based on ensemble model output statistics ⋮ Kernel mixture model for probability density estimation in Bayesian classifiers ⋮ Probabilistic forecasting of wind power ramp events using autoregressive logit models ⋮ Wind energy: forecasting challenges for its operational management ⋮ Nonparametric Regression for Spherical Data ⋮ Catching Uncertainty of Wind: A Blend of Sieve Bootstrap and Regime Switching Models for Probabilistic Short-Term Forecasting of Wind Speed ⋮ Hierarchical Probabilistic Forecasting of Electricity Demand With Smart Meter Data

Cites Work

• Making and Evaluating Point Forecasts
• Bootstrap prediction for returns and volatilities in GARCH models
• Testing for a unit root nonstationarity in multivariate autoregressive time series
• Computationally efficient bootstrap prediction intervals for returns and volatilities in ARCH and GARCH processes
• A crossvalidation method for estimating conditional densities
• Robust Likelihood Methods Based on the Skew-t and Related Distributions
• Nonparametric Estimation and Symmetry Tests for Conditional Density Functions
• Powering Up With Space-Time Wind Forecasting
• Probabilistic Forecasts, Calibration and Sharpness
• Remarks on Non-Parametric Estimates for Density Functions and Regression Curves
• Calibrated Probabilistic Forecasting at the Stateline Wind Energy Center
• Bandwidth selection for kernel conditional density estimation.

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